Existence and Learning of Oscillations in Recurrent Neural Networks  

Oleh:

Townley, S. ; Ilchmann, A. ; Weiss, M. G. ; Mcclements, W. ; Ruiz, A. C. ; Owens, D. H. ; Pratzel-Wolters, D.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 1 (2000), page 205-214.
Topik: oscillations; learning; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We study a particular class of n - node recurrent neural networks (RNN s). In the 3 - node case we use monotone dynamical systems theory to show, for a well - defined set of parameters, that, generically, every orbit of the RNN is asymptotic to a periodic orbit. We then investigate whether RNN s of this class can adapt their internal parameters so as to “learn” and then replicate autonomously (in feedback) certain external periodic signals. Our learning algorithm is similar to the identification algorithms in adaptive control theory. The main feature of the algorithm is that global exponential convergence of parameters is guaranteed. We also obtain partial convergence results in the n - node case.

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